backtesting-trading-strategies

Files (12)

• SKILL.md
• settings.yaml · config/
• BTC_USD_1d.csv · data/
• ETH_USD_1d.csv · data/
• errors.md · references/
• examples.md · references/
• implementation.md · references/
• backtest.py · scripts/
• fetch_data.py · scripts/
• metrics.py · scripts/
• optimize.py · scripts/
• strategies.py · scripts/

SKILL.md (first lines)

# Backtesting Trading Strategies

## Overview

Validate trading strategies against historical data before risking real capital. This skill provides a complete backtesting framework with 8 built-in strategies, comprehensive performance metrics, and parameter optimization.

**Key Features:**
- 8 pre-built trading strategies (SMA, EMA, RSI, MACD, Bollinger, Breakout, Mean Reversion, Momentum)
- Full performance metrics (Sharpe, Sortino, Calmar, VaR, max drawdown)
- Parameter grid search optimization
- Equity curve visualization
- Trade-by-trade analysis

## Prerequisites

Install required dependencies:

```bash
pip install pandas numpy yfinance matplotlib
```

Optional for advanced features:
```bash
pip install ta-lib scipy scikit-learn
```

## Instructions

### Step 1: Fetch Historical Data

```bash
python {baseDir}/scripts/fetch_data.py --symbol BTC-USD --period 2y --interval 1d
```

Data is cached to `{baseDir}/data/{symbol}_{interval}.csv` for reuse.

### Step 2: Run Backtest

Basic backtest with default parameters:

```bash
python {baseDir}/scripts/backtest.py --strategy sma_crossover --symbol BTC-USD --period 1y
```

Advanced backtest with custom parameters:

```bash
# Example: backtest with specific date range
python {baseDir}/scripts/backtest.py \
  --strategy rsi_reversal \
  --symbol ETH-USD \
  --period 1y \
  --capital 10000 \
  --params '{"period": 14, "overbought": 70, "oversold": 30}'
```

### Step 3: Analyze Results

Results are saved to `{baseDir}/reports/` including:
- `*_summary.txt` - Performance metrics
- `*_trades.csv` - Trade log
- `*_equity.csv` - Equity curve data
- `*_chart.png` - Visual equity curve

### Step 4: Optimize Parameters

Find optimal parameters via grid search:

```bash
python {baseDir}/scripts/optimize.py \
  --strategy sma_crossover \
  --symbol BTC-USD \
  --period 1y \
  --param-grid '{"fast_period": [10, 20, 30], "slow_period": [50, 100, 200]}'
```

## Output

### Performance Metrics

| Metric | Description |
|--------|-------------|
| Total Return | Overall percentage gain/loss |
| CAGR | Compound annual growth rate |
| Sharpe Ratio | Risk-adjusted return (target: >1.5) |
| Sortino Ratio | Downside risk-adjusted return |
| Calmar Ratio | Return divided by max drawdown |

### Risk Metrics

| Metric | Description |
|--------|-------------|
| Max Drawdown | Largest peak-to-trough decline |
| VaR (95%) | Value at Risk at 95% confidence |
| CVaR (95%) | Expected loss beyond VaR |
| Volatility | Annualized standard deviation |

### Trade Statistics

| Metric | Description |
|--------|-------------|
| Total Trades | Number of round-trip trades |
| Win Rate | Percentage of profitable trades |
| Profit Factor | Gross profit divided by gross loss |
| Expectancy | Expected value per trade |

### Example Output

```
================================================================================
                    BACKTEST RESULTS: SMA CROSSOVER
                    BTC-USD | [start_date] to [end_date]
================================================================================
 PERFORMANCE                          | RISK
 Total Return:        +47.32%         | Max Drawdown:      -18.45%
 CAGR:                +47.32%         | VaR (95%):         -2.34%
 Sharpe Ratio:        1.87            | Volatility:        42.1%
 Sortino Ratio:       2.41            | Ulcer Index:       8.2
--------------------------------------------------------------------------------

From aAAaqwq/AGI-Super-Team, MIT.

More from aAAaqwq/AGI-Super-Team